Yuzhe Qin
Entong Su
Description: Tactile RL
reinforcement learning (154) tactile (81)
Using Tactile sensors for manipulation remains as one of the most challenging problems in robotics. At the heart of these challenges is generalization: How can we train a tactile-based policy that can manipulate unseen and diverse objects? In this paper, we propose to perform Reinforcement Learning with only visual tactile sensing inputs on diverse objects in a physical simulator. By training with large-scale data in simulation, it enables the policy to generalize to unseen objects. However, leveraging simu
We use tactile images to train the reinforcement learning policy for pivoting tasks. The right-side tactile images in the video depict the RGB , difference image , and binary image from top to bottom.
We train dexterous manipulation using RL with single-viewed point cloud. The blue points are observed by camera while the red points are imagined from robot model.
Yuzhe Qin
Entong Su